My research interests lie in the area of large-scale data science, i.e., I work on efficiently analyzing huge amounts of data given limited compute resources. I work together with physicists to improve the classification accuracy of systems for detecting, e.g., new stars or distant galaxies by incorporating huge amounts of image data into the training phase of appropriate machine learning models. Other ongoing projects conducted together with researchers from the field of remote sensing aim at efficiently analyzing satellite images via, e.g., deep convolutional neural networks or specialized change detection algorithms. Nowadays, such tasks often involve the analysis of huge amounts of data in the tera- or even petabyte range and I work on overcoming such challenges by developing efficient schemes that are adapted to the specific needs of the tasks at hand. In particular, I make use of techniques and tools from the fields of high-performance computing (e.g., GPGPU programming) and distributed computing (e.g., Apache Spark) to reduce the practical runtime of the involved methods. In many cases, the original algorithmic building blocks are not suited for modern (massively-parallel) hardware architectures. The adaptation of known and the development of new methods that can effectively deal with huge amounts of data are part of my main research activities.